The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 무선 센서 네트워크의 클러스터 내에서 영상 압축 작업의 작업량을 분산시켜 센서 노드 간의 에너지 소비 균형을 맞추는 두 가지 알고리즘을 제안합니다. 제안된 알고리즘의 주요 요점은 센서 노드 간의 작업 교환 및/또는 할당을 구현할 때 사용되는 에너지 임계값을 채택하는 것입니다. 임계값은 센서 노드의 잔류 에너지, 입력 이미지, 압축 출력 및 네트워크 매개변수에 잘 적응합니다. 제안된 알고리즘에 이산 코사인 변환의 확장 버전인 중첩 변환 기법과 Lempel-Ziv-Welch 코딩 이전의 런 길이 인코딩을 적용하여 영상 압축 방식의 품질과 압축률을 모두 향상시켰다. 우리는 방법을 검증하기 위해 광범위하게 계산 실험을 수행하고 제안된 알고리즘이 센서 노드 간의 총 에너지 소비 균형을 유지하여 전체 네트워크 수명을 늘릴 뿐만 아니라 이미지 압축에서 블록 노이즈를 줄이는 것을 확인했습니다.
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Phat NGUYEN HUU, Vinh TRAN-QUANG, Takumi MIYOSHI, "Low-Complexity and Energy-Efficient Algorithms on Image Compression for Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications,
vol. E93-B, no. 12, pp. 3438-3447, December 2010, doi: 10.1587/transcom.E93.B.3438.
Abstract: This paper proposes two algorithms to balance energy consumption among sensor nodes by distributing the workload of image compression tasks within a cluster on wireless sensor networks. The main point of the proposed algorithms is to adopt the energy threshold, which is used when we implement the exchange and/or assignment of tasks among sensor nodes. The threshold is well adaptive to the residual energy of sensor nodes, input image, compressed output, and network parameters. We apply the lapped transform technique, an extended version of the discrete cosine transform, and run length encoding before Lempel-Ziv-Welch coding to the proposed algorithms to improve both quality and compression rate in image compression scheme. We extensively conduct computational experiments to verify the our methods and find that the proposed algorithms achieve not only balancing the total energy consumption among sensor nodes and, thus, increasing the overall network lifetime, but also reducing block noise in image compression.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E93.B.3438/_p
부
@ARTICLE{e93-b_12_3438,
author={Phat NGUYEN HUU, Vinh TRAN-QUANG, Takumi MIYOSHI, },
journal={IEICE TRANSACTIONS on Communications},
title={Low-Complexity and Energy-Efficient Algorithms on Image Compression for Wireless Sensor Networks},
year={2010},
volume={E93-B},
number={12},
pages={3438-3447},
abstract={This paper proposes two algorithms to balance energy consumption among sensor nodes by distributing the workload of image compression tasks within a cluster on wireless sensor networks. The main point of the proposed algorithms is to adopt the energy threshold, which is used when we implement the exchange and/or assignment of tasks among sensor nodes. The threshold is well adaptive to the residual energy of sensor nodes, input image, compressed output, and network parameters. We apply the lapped transform technique, an extended version of the discrete cosine transform, and run length encoding before Lempel-Ziv-Welch coding to the proposed algorithms to improve both quality and compression rate in image compression scheme. We extensively conduct computational experiments to verify the our methods and find that the proposed algorithms achieve not only balancing the total energy consumption among sensor nodes and, thus, increasing the overall network lifetime, but also reducing block noise in image compression.},
keywords={},
doi={10.1587/transcom.E93.B.3438},
ISSN={1745-1345},
month={December},}
부
TY - JOUR
TI - Low-Complexity and Energy-Efficient Algorithms on Image Compression for Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 3438
EP - 3447
AU - Phat NGUYEN HUU
AU - Vinh TRAN-QUANG
AU - Takumi MIYOSHI
PY - 2010
DO - 10.1587/transcom.E93.B.3438
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E93-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2010
AB - This paper proposes two algorithms to balance energy consumption among sensor nodes by distributing the workload of image compression tasks within a cluster on wireless sensor networks. The main point of the proposed algorithms is to adopt the energy threshold, which is used when we implement the exchange and/or assignment of tasks among sensor nodes. The threshold is well adaptive to the residual energy of sensor nodes, input image, compressed output, and network parameters. We apply the lapped transform technique, an extended version of the discrete cosine transform, and run length encoding before Lempel-Ziv-Welch coding to the proposed algorithms to improve both quality and compression rate in image compression scheme. We extensively conduct computational experiments to verify the our methods and find that the proposed algorithms achieve not only balancing the total energy consumption among sensor nodes and, thus, increasing the overall network lifetime, but also reducing block noise in image compression.
ER -